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allcatclf.py
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import os
from collections import defaultdict
import shutil
import sys
import random
from models import tf_idf, w2v_w_tf_idf
def rem_acc(acc_train, acc_test, label):
if acc_train > stat[label]['train']['max']:
stat[label]['train']['max'] = acc_train
if acc_train < stat[label]['train']['min']:
stat[label]['train']['min'] = acc_train
if acc_test > stat[label]['test']['max']:
stat[label]['test']['max'] = acc_test
if acc_test < stat[label]['test']['min']:
stat[label]['test']['min'] = acc_test
stat[label]['train']['avg'] += acc_train
stat[label]['test']['avg'] += acc_test
return
# Выбор каталога данных
root = os.path.join(os.curdir, 'data')
category_names = [category for category in os.listdir(root)]
combinations = defaultdict(list)
for i in range(2, len(category_names) + 1):
for j in range(20):
combinations[i].append(random.sample(category_names, k=i))
total_numb_combinations = 0
for i in range(2, len(category_names) + 1):
print('Numb. of categories\t|\tNumb. of combinations')
print('{}:\t\t\t{}'.format(i, len(combinations[i])))
total_numb_combinations += len(combinations[i])
print('')
for i in range(2, len(category_names) + 1):
print('Numb. of categories\t|\tCombinations')
print('{}:\t\t\t{}'.format(i, combinations[i]))
print('')
# exit(0)
with open('all_cat_res.txt', 'a') as results:
results.write('n_categories\t' +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['svm_lin_tfidf'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['svm_lin_tfidf_w2v'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['svm_lin_tfidf_w2v_c'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['svm_rbf_tfidf'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['svm_rbf_tfidf_w2v'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['svm_rbf_tfidf_w2v_c'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['knn_tfidf'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['knn_tfidf_w2v'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['knn_tfidf_w2v_c'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['rfc_tfidf'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['rfc_tfidf_w2v'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['rfc_tfidf_w2v_c'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['mlp_relu_tfidf'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['mlp_relu_tfidf_w2v'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['mlp_relu_tfidf_w2v_c'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['mlp_logistic_tfidf'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['mlp_logistic_tfidf_w2v'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['mlp_logistic_tfidf_w2v_c'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['mlp_tanh_tfidf'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\t'.format(
*['mlp_tanh_tfidf_w2v'] * 6) +
'{}_train_max\t{}_train_avg\t{}_train_min\t{}_test_max\t{}_test_avg\t{}_test_min\n'.format(
*['mlp_tanh_tfidf_w2v_c'] * 6)
)
numb_combinations = 0
for key in sorted(combinations.keys()):
stat = {'SVM_LIN_TFIDF': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'SVM_LIN_TFIDF_W2V': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'SVM_LIN_TFIDF_W2V_K': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'SVM_RBF_TFIDF': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'SVM_RBF_TFIDF_W2V': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'SVM_RBF_TFIDF_W2V_K': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'KNN_TFIDF': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'KNN_TFIDF_W2V': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'KNN_TFIDF_W2V_K': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'RFC_TFIDF': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'RFC_TFIDF_W2V': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'RFC_TFIDF_W2V_K': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'MLP_TFIDF': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'MLP_TFIDF_W2V': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'MLP_TFIDF_W2V_K': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'MLP_LOGIST_TFIDF': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'MLP_LOGIST_TFIDF_W2V': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'MLP_LOGIST_TFIDF_W2V_K': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'MLP_TANH_TFIDF': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'MLP_TANH_TFIDF_W2V': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
'MLP_TANH_TFIDF_W2V_K': {'train': {'max': 0, 'min': sys.maxsize, 'avg': 0},
'test': {'max': 0, 'min': sys.maxsize, 'avg': 0}},
}
k = 0
for combination in combinations[key]:
if os.path.exists('_tmp_'):
shutil.rmtree('_tmp_')
os.mkdir('_tmp_')
for category_name in list(combination):
sub_folder = os.path.join('_tmp_', category_name)
shutil.copytree(os.path.join(root, category_name), sub_folder)
rem_acc(*tf_idf(data_dir_path='_tmp_', classifier='SVM', svm_kernel='linear', verbose=True),
label='SVM_LIN_TFIDF')
rem_acc(*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='SVM', concatenate=False, svm_kernel='linear',
verbose=True), label='SVM_LIN_TFIDF_W2V')
rem_acc(
*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='SVM', concatenate=True, svm_kernel='linear', verbose=True),
label='SVM_LIN_TFIDF_W2V_K')
rem_acc(*tf_idf(data_dir_path='_tmp_', classifier='SVM', svm_kernel='rbf', verbose=True), label='SVM_RBF_TFIDF')
rem_acc(
*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='SVM', concatenate=False, svm_kernel='rbf', verbose=True),
label='SVM_RBF_TFIDF_W2V')
rem_acc(
*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='SVM', concatenate=True, svm_kernel='rbf', verbose=True),
label='SVM_RBF_TFIDF_W2V_K')
rem_acc(*tf_idf(data_dir_path='_tmp_', classifier='KNN', verbose=True), label='KNN_TFIDF')
rem_acc(*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='KNN', concatenate=False, verbose=True),
label='KNN_TFIDF_W2V')
rem_acc(*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='KNN', concatenate=True, verbose=True),
label='KNN_TFIDF_W2V_K')
rem_acc(*tf_idf(data_dir_path='_tmp_', classifier='RFC', verbose=True), label='RFC_TFIDF')
rem_acc(*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='RFC', concatenate=False, verbose=True),
label='RFC_TFIDF_W2V')
rem_acc(*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='RFC', concatenate=True, verbose=True),
label='RFC_TFIDF_W2V_K')
rem_acc(*tf_idf(data_dir_path='_tmp_', classifier='MLP', mlp_activation='relu', verbose=True),
label='MLP_TFIDF')
rem_acc(*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='MLP', concatenate=False, mlp_activation='relu',
verbose=True), label='MLP_TFIDF_W2V')
rem_acc(*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='MLP', concatenate=True, mlp_activation='relu',
verbose=True), label='MLP_TFIDF_W2V_K')
rem_acc(*tf_idf(data_dir_path='_tmp_', classifier='MLP', mlp_activation='logistic', verbose=True),
label='MLP_LOGIST_TFIDF')
rem_acc(*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='MLP', concatenate=False, mlp_activation='logistic',
verbose=True), label='MLP_LOGIST_TFIDF_W2V')
rem_acc(*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='MLP', concatenate=True, mlp_activation='logistic',
verbose=True), label='MLP_LOGIST_TFIDF_W2V_K')
rem_acc(*tf_idf(data_dir_path='_tmp_', classifier='MLP', mlp_activation='tanh', verbose=True),
label='MLP_TANH_TFIDF')
rem_acc(*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='MLP', concatenate=False, mlp_activation='tanh',
verbose=True), label='MLP_TANH_TFIDF_W2V')
rem_acc(*w2v_w_tf_idf(data_dir_path='_tmp_', classifier='MLP', concatenate=True, mlp_activation='tanh',
verbose=True), label='MLP_TANH_TFIDF_W2V_K')
k += 1
shutil.rmtree('_tmp_')
numb_combinations += 1
print('||||||||||||||||||||||||||||||||||\t{:.3f}%\t||||||||||||||||||||||||||||||||||'.format(
numb_combinations / total_numb_combinations * 100
))
for label in stat.keys():
stat[label]['train']['avg'] /= k
stat[label]['test']['avg'] /= k
with open('all_cat_res.txt', 'a') as results:
results.write('{}\t'.format(key) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['SVM_LIN_TFIDF']['train']['max'],
stat['SVM_LIN_TFIDF']['train']['avg'],
stat['SVM_LIN_TFIDF']['train']['min'],
stat['SVM_LIN_TFIDF']['test']['max'],
stat['SVM_LIN_TFIDF']['test']['avg'],
stat['SVM_LIN_TFIDF']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['SVM_LIN_TFIDF_W2V']['train']['max'],
stat['SVM_LIN_TFIDF_W2V']['train']['avg'],
stat['SVM_LIN_TFIDF_W2V']['train']['min'],
stat['SVM_LIN_TFIDF_W2V']['test']['max'],
stat['SVM_LIN_TFIDF_W2V']['test']['avg'],
stat['SVM_LIN_TFIDF_W2V']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['SVM_LIN_TFIDF_W2V_K']['train']['max'],
stat['SVM_LIN_TFIDF_W2V_K']['train']['avg'],
stat['SVM_LIN_TFIDF_W2V_K']['train']['min'],
stat['SVM_LIN_TFIDF_W2V_K']['test']['max'],
stat['SVM_LIN_TFIDF_W2V_K']['test']['avg'],
stat['SVM_LIN_TFIDF_W2V_K']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['SVM_RBF_TFIDF']['train']['max'],
stat['SVM_RBF_TFIDF']['train']['avg'],
stat['SVM_RBF_TFIDF']['train']['min'],
stat['SVM_RBF_TFIDF']['test']['max'],
stat['SVM_RBF_TFIDF']['test']['avg'],
stat['SVM_RBF_TFIDF']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['SVM_RBF_TFIDF_W2V']['train']['max'],
stat['SVM_RBF_TFIDF_W2V']['train']['avg'],
stat['SVM_RBF_TFIDF_W2V']['train']['min'],
stat['SVM_RBF_TFIDF_W2V']['test']['max'],
stat['SVM_RBF_TFIDF_W2V']['test']['avg'],
stat['SVM_RBF_TFIDF_W2V']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['SVM_RBF_TFIDF_W2V_K']['train']['max'],
stat['SVM_RBF_TFIDF_W2V_K']['train']['avg'],
stat['SVM_RBF_TFIDF_W2V_K']['train']['min'],
stat['SVM_RBF_TFIDF_W2V_K']['test']['max'],
stat['SVM_RBF_TFIDF_W2V_K']['test']['avg'],
stat['SVM_RBF_TFIDF_W2V_K']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['KNN_TFIDF']['train']['max'],
stat['KNN_TFIDF']['train']['avg'],
stat['KNN_TFIDF']['train']['min'],
stat['KNN_TFIDF']['test']['max'],
stat['KNN_TFIDF']['test']['avg'],
stat['KNN_TFIDF']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['KNN_TFIDF_W2V']['train']['max'],
stat['KNN_TFIDF_W2V']['train']['avg'],
stat['KNN_TFIDF_W2V']['train']['min'],
stat['KNN_TFIDF_W2V']['test']['max'],
stat['KNN_TFIDF_W2V']['test']['avg'],
stat['KNN_TFIDF_W2V']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['KNN_TFIDF_W2V_K']['train']['max'],
stat['KNN_TFIDF_W2V_K']['train']['avg'],
stat['KNN_TFIDF_W2V_K']['train']['min'],
stat['KNN_TFIDF_W2V_K']['test']['max'],
stat['KNN_TFIDF_W2V_K']['test']['avg'],
stat['KNN_TFIDF_W2V_K']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['RFC_TFIDF']['train']['max'],
stat['RFC_TFIDF']['train']['avg'],
stat['RFC_TFIDF']['train']['min'],
stat['RFC_TFIDF']['test']['max'],
stat['RFC_TFIDF']['test']['avg'],
stat['RFC_TFIDF']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['RFC_TFIDF_W2V']['train']['max'],
stat['RFC_TFIDF_W2V']['train']['avg'],
stat['RFC_TFIDF_W2V']['train']['min'],
stat['RFC_TFIDF_W2V']['test']['max'],
stat['RFC_TFIDF_W2V']['test']['avg'],
stat['RFC_TFIDF_W2V']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['RFC_TFIDF_W2V_K']['train']['max'],
stat['RFC_TFIDF_W2V_K']['train']['avg'],
stat['RFC_TFIDF_W2V_K']['train']['min'],
stat['RFC_TFIDF_W2V_K']['test']['max'],
stat['RFC_TFIDF_W2V_K']['test']['avg'],
stat['RFC_TFIDF_W2V_K']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['MLP_TFIDF']['train']['max'],
stat['MLP_TFIDF']['train']['avg'],
stat['MLP_TFIDF']['train']['min'],
stat['MLP_TFIDF']['test']['max'],
stat['MLP_TFIDF']['test']['avg'],
stat['MLP_TFIDF']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['MLP_TFIDF_W2V']['train']['max'],
stat['MLP_TFIDF_W2V']['train']['avg'],
stat['MLP_TFIDF_W2V']['train']['min'],
stat['MLP_TFIDF_W2V']['test']['max'],
stat['MLP_TFIDF_W2V']['test']['avg'],
stat['MLP_TFIDF_W2V']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['MLP_TFIDF_W2V_K']['train']['max'],
stat['MLP_TFIDF_W2V_K']['train']['avg'],
stat['MLP_TFIDF_W2V_K']['train']['min'],
stat['MLP_TFIDF_W2V_K']['test']['max'],
stat['MLP_TFIDF_W2V_K']['test']['avg'],
stat['MLP_TFIDF_W2V_K']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['MLP_LOGIST_TFIDF']['train']['max'],
stat['MLP_LOGIST_TFIDF']['train']['avg'],
stat['MLP_LOGIST_TFIDF']['train']['min'],
stat['MLP_LOGIST_TFIDF']['test']['max'],
stat['MLP_LOGIST_TFIDF']['test']['avg'],
stat['MLP_LOGIST_TFIDF']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['MLP_LOGIST_TFIDF_W2V']['train']['max'],
stat['MLP_LOGIST_TFIDF_W2V']['train']['avg'],
stat['MLP_LOGIST_TFIDF_W2V']['train']['min'],
stat['MLP_LOGIST_TFIDF_W2V']['test']['max'],
stat['MLP_LOGIST_TFIDF_W2V']['test']['avg'],
stat['MLP_LOGIST_TFIDF_W2V']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['MLP_LOGIST_TFIDF_W2V_K']['train']['max'],
stat['MLP_LOGIST_TFIDF_W2V_K']['train']['avg'],
stat['MLP_LOGIST_TFIDF_W2V_K']['train']['min'],
stat['MLP_LOGIST_TFIDF_W2V_K']['test']['max'],
stat['MLP_LOGIST_TFIDF_W2V_K']['test']['avg'],
stat['MLP_LOGIST_TFIDF_W2V_K']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['MLP_TANH_TFIDF']['train']['max'],
stat['MLP_TANH_TFIDF']['train']['avg'],
stat['MLP_TANH_TFIDF']['train']['min'],
stat['MLP_TANH_TFIDF']['test']['max'],
stat['MLP_TANH_TFIDF']['test']['avg'],
stat['MLP_TANH_TFIDF']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\t'.format(stat['MLP_TANH_TFIDF_W2V']['train']['max'],
stat['MLP_TANH_TFIDF_W2V']['train']['avg'],
stat['MLP_TANH_TFIDF_W2V']['train']['min'],
stat['MLP_TANH_TFIDF_W2V']['test']['max'],
stat['MLP_TANH_TFIDF_W2V']['test']['avg'],
stat['MLP_TANH_TFIDF_W2V']['test']['min']) +
'{}\t{}\t{}\t{}\t{}\t{}\n'.format(stat['MLP_TANH_TFIDF_W2V_K']['train']['max'],
stat['MLP_TANH_TFIDF_W2V_K']['train']['avg'],
stat['MLP_TANH_TFIDF_W2V_K']['train']['min'],
stat['MLP_TANH_TFIDF_W2V_K']['test']['max'],
stat['MLP_TANH_TFIDF_W2V_K']['test']['avg'],
stat['MLP_TANH_TFIDF_W2V_K']['test']['min'])
)